Overview

Dataset statistics

Number of variables51
Number of observations50000
Missing cells746345
Missing cells (%)29.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.5 MiB
Average record size in memory408.0 B

Variable types

CAT28
NUM19
BOOL4

Warnings

Visibility has constant value "50000" Constant
CONNECTIONDETAIL has a high cardinality: 773 distinct values High cardinality
FILENAME_INGEST has a high cardinality: 5454 distinct values High cardinality
IP_DST has a high cardinality: 4521 distinct values High cardinality
IP_SRC has a high cardinality: 19797 distinct values High cardinality
LOCAL_TIMESTAMP has a high cardinality: 43546 distinct values High cardinality
MESSAGE has a high cardinality: 4260 distinct values High cardinality
TIME_RECEIPT has a high cardinality: 49932 distinct values High cardinality
TUNNELPARENTUUIDS has a high cardinality: 150 distinct values High cardinality
UUID_BRO has a high cardinality: 43576 distinct values High cardinality
COUNT_BYTES_IN_ONTHEWIRE is highly correlated with COUNT_BYTES_IN and 1 other fieldsHigh correlation
COUNT_BYTES_IN is highly correlated with COUNT_BYTES_IN_ONTHEWIRE and 2 other fieldsHigh correlation
COUNT_PACKETS_DST is highly correlated with COUNT_BYTES_IN and 3 other fieldsHigh correlation
COUNT_BYTES_OUT_ONTHEWIRE is highly correlated with COUNT_PACKETS_DST and 1 other fieldsHigh correlation
COUNT_PACKETS_SRC is highly correlated with COUNT_BYTES_IN and 2 other fieldsHigh correlation
LONGITUDE_SRC is highly correlated with LATITUDE_SRCHigh correlation
LATITUDE_SRC is highly correlated with LONGITUDE_SRCHigh correlation
NUMPACKETS_LOSS is highly correlated with NUMPACKETS_ACK and 1 other fieldsHigh correlation
NUMPACKETS_ACK is highly correlated with NUMPACKETS_LOSS and 1 other fieldsHigh correlation
NUMPACKETS_LOSS_PERCENT is highly correlated with NUMPACKETS_ACK and 1 other fieldsHigh correlation
APPLICATIONPROTOCOL has 49512 (99.0%) missing values Missing
ASN_DST has 7611 (15.2%) missing values Missing
ASN_SRC has 12658 (25.3%) missing values Missing
COCOM_DST has 7305 (14.6%) missing values Missing
COCOM_SRC has 11210 (22.4%) missing values Missing
CONNECTIONDETAIL has 6920 (13.8%) missing values Missing
CONNECTIONORIGIN_SRC has 6424 (12.8%) missing values Missing
CONNECTIONSTATE_BRO has 6424 (12.8%) missing values Missing
COUNTRY_DST has 7299 (14.6%) missing values Missing
COUNTRY_SRC has 11210 (22.4%) missing values Missing
COUNT_BYTES_IN has 9809 (19.6%) missing values Missing
COUNT_BYTES_IN_ONTHEWIRE has 6424 (12.8%) missing values Missing
COUNT_BYTES_MISSING has 6424 (12.8%) missing values Missing
COUNT_BYTES_OUT has 9809 (19.6%) missing values Missing
COUNT_BYTES_OUT_ONTHEWIRE has 6424 (12.8%) missing values Missing
COUNT_PACKETS_DST has 6424 (12.8%) missing values Missing
COUNT_PACKETS_SRC has 6424 (12.8%) missing values Missing
DURATION has 9809 (19.6%) missing values Missing
DURATION_LOG has 49995 (> 99.9%) missing values Missing
IPBRANCHCATEGORY_DST has 7112 (14.2%) missing values Missing
IPBRANCHCATEGORY_SRC has 11210 (22.4%) missing values Missing
IP_DST has 6424 (12.8%) missing values Missing
IP_SRC has 6424 (12.8%) missing values Missing
LATITUDE_DST has 7253 (14.5%) missing values Missing
LATITUDE_SRC has 11210 (22.4%) missing values Missing
LOCAL_TIMESTAMP has 6448 (12.9%) missing values Missing
LONGITUDE_DST has 7253 (14.5%) missing values Missing
LONGITUDE_SRC has 11210 (22.4%) missing values Missing
MESSAGE has 43581 (87.2%) missing values Missing
MSGSOURCE_BRO has 43581 (87.2%) missing values Missing
NUMPACKETS_ACK has 49995 (> 99.9%) missing values Missing
NUMPACKETS_LOSS has 49995 (> 99.9%) missing values Missing
NUMPACKETS_LOSS_PERCENT has 49995 (> 99.9%) missing values Missing
ORGANIZATION_OWNER_DST has 16526 (33.1%) missing values Missing
ORGANIZATION_OWNER_SRC has 11565 (23.1%) missing values Missing
PEERNAME has 43576 (87.2%) missing values Missing
PORT_DST has 6424 (12.8%) missing values Missing
PORT_SRC has 6424 (12.8%) missing values Missing
RESPONSEORIGIN_DST has 6424 (12.8%) missing values Missing
SEVERITY_MESSAGE has 43581 (87.2%) missing values Missing
TRANSPORTPROTOCOL has 6424 (12.8%) missing values Missing
TUNNELPARENTUUIDS has 49171 (98.3%) missing values Missing
UUID_BRO has 6424 (12.8%) missing values Missing
COUNT_BYTES_IN is highly skewed (γ1 = 147.4936546) Skewed
COUNT_BYTES_IN_ONTHEWIRE is highly skewed (γ1 = 132.3513756) Skewed
COUNT_BYTES_MISSING is highly skewed (γ1 = 74.33494996) Skewed
COUNT_BYTES_OUT is highly skewed (γ1 = 197.963122) Skewed
COUNT_BYTES_OUT_ONTHEWIRE is highly skewed (γ1 = 183.7178112) Skewed
COUNT_PACKETS_DST is highly skewed (γ1 = 167.6830686) Skewed
COUNT_PACKETS_SRC is highly skewed (γ1 = 187.0908029) Skewed
DURATION is highly skewed (γ1 = 147.1346037) Skewed
LOCAL_TIMESTAMP is uniformly distributed Uniform
TIME_RECEIPT is uniformly distributed Uniform
UUID_BRO is uniformly distributed Uniform
Id has unique values Unique
COUNT_BYTES_IN has 7119 (14.2%) zeros Zeros
COUNT_BYTES_IN_ONTHEWIRE has 7121 (14.2%) zeros Zeros
COUNT_BYTES_MISSING has 42079 (84.2%) zeros Zeros
COUNT_BYTES_OUT has 3820 (7.6%) zeros Zeros
COUNT_PACKETS_DST has 7121 (14.2%) zeros Zeros

Reproduction

Analysis started2020-09-27 00:21:09.364203
Analysis finished2020-09-27 00:22:30.476344
Duration1 minute and 21.11 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Id
Categorical

UNIQUE

Distinct50000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size390.6 KiB
C02T641ElYCCkPQM0d
 
1
C0Ggaw1sl5r9ulrUr
 
1
C0A9jt1A03AqWyUXu6
 
1
C0AQhri2n6PgTs0y8
 
1
C0IcXh37lCyxgK8zT2
 
1
Other values (49995)
49995 
ValueCountFrequency (%) 
C02T641ElYCCkPQM0d1< 0.1%
 
C0Ggaw1sl5r9ulrUr1< 0.1%
 
C0A9jt1A03AqWyUXu61< 0.1%
 
C0AQhri2n6PgTs0y81< 0.1%
 
C0IcXh37lCyxgK8zT21< 0.1%
 
C0HCR14SroAHVkT4X71< 0.1%
 
C0LIl94o4xfyywAM361< 0.1%
 
c3c73da459115b7e730df1abfbf4d20b1< 0.1%
 
19109bf416c7981f8faf2767141d66ab1< 0.1%
 
C0Gq6l4iLBj4NYAGQ71< 0.1%
 
Other values (49990)49990> 99.9%
 
2020-09-26T20:22:30.675811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique50000 ?
Unique (%)100.0%
2020-09-26T20:22:30.826439image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length32
Median length18
Mean length19.56816
Min length15

Timestamp
Real number (ℝ≥0)

Distinct49932
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.596695559e+12
Minimum1.596326402e+12
Maximum1.59693119e+12
Zeros0
Zeros (%)0.0%
Memory size390.6 KiB
2020-09-26T20:22:30.988516image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.596326402e+12
5-th percentile1.596340139e+12
Q11.596394845e+12
median1.596803646e+12
Q31.596871824e+12
95-th percentile1.596919144e+12
Maximum1.59693119e+12
Range604787859
Interquartile range (IQR)476979580.8

Descriptive statistics

Standard deviation226137106.1
Coefficient of variation (CV)0.0001416281926
Kurtosis-1.360424371
Mean1.596695559e+12
Median Absolute Deviation (MAD)83517123
Skewness-0.6928314294
Sum7.983477796e+16
Variance5.113799077e+16
MonotocityNot monotonic
2020-09-26T20:22:31.427403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.596760674e+122< 0.1%
 
1.596839582e+122< 0.1%
 
1.596830582e+122< 0.1%
 
1.596853356e+122< 0.1%
 
1.596798516e+122< 0.1%
 
1.596384575e+122< 0.1%
 
1.596369643e+122< 0.1%
 
1.596771978e+122< 0.1%
 
1.596856856e+122< 0.1%
 
1.596800304e+122< 0.1%
 
Other values (49922)49980> 99.9%
 
ValueCountFrequency (%) 
1.596326402e+121< 0.1%
 
1.596326404e+121< 0.1%
 
1.596326411e+121< 0.1%
 
1.596326412e+121< 0.1%
 
1.59632642e+121< 0.1%
 
ValueCountFrequency (%) 
1.59693119e+121< 0.1%
 
1.596931186e+121< 0.1%
 
1.596931169e+121< 0.1%
 
1.596931168e+121< 0.1%
 
1.596931164e+121< 0.1%
 

Data Type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size390.6 KiB
bro-e-conn
43576 
bro-e-communication
6419 
bro-e-capture_loss
 
5
ValueCountFrequency (%) 
bro-e-conn4357687.2%
 
bro-e-communication641912.8%
 
bro-e-capture_loss5< 0.1%
 
2020-09-26T20:22:31.591845image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-26T20:22:31.677615image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:22:31.785399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length10
Mean length11.15622
Min length10

Visibility
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size390.6 KiB
U&FOUO
50000 
ValueCountFrequency (%) 
U&FOUO50000100.0%
 
2020-09-26T20:22:31.902052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-26T20:22:31.986902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:22:32.065690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length6
Mean length6
Min length6

APPLICATIONPROTOCOL
Categorical

MISSING

Distinct15
Distinct (%)3.1%
Missing49512
Missing (%)99.0%
Memory size390.6 KiB
ssl
163 
dns
160 
krb_tcp
66 
gssapi
56 
http
 
8
Other values (10)
35 
ValueCountFrequency (%) 
ssl1630.3%
 
dns1600.3%
 
krb_tcp660.1%
 
gssapi560.1%
 
http8< 0.1%
 
ssh7< 0.1%
 
gssapi,smb7< 0.1%
 
ntlm,dce_rpc4< 0.1%
 
smb,gssapi4< 0.1%
 
dce_rpc,ntlm3< 0.1%
 
Other values (5)10< 0.1%
 
(Missing)4951299.0%
 
2020-09-26T20:22:32.205316image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)0.4%
2020-09-26T20:22:32.344944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length23
Median length3
Mean length3.0132
Min length3

ASN_DST
Real number (ℝ≥0)

MISSING

Distinct177
Distinct (%)0.4%
Missing7611
Missing (%)15.2%
Infinite0
Infinite (%)0.0%
Mean3791.493265
Minimum71
Maximum394353
Zeros0
Zeros (%)0.0%
Memory size390.6 KiB
2020-09-26T20:22:32.482574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum71
5-th percentile531
Q1721
median1569
Q31585
95-th percentile16509
Maximum394353
Range394282
Interquartile range (IQR)864

Descriptive statistics

Standard deviation7334.624601
Coefficient of variation (CV)1.934494957
Kurtosis427.268434
Mean3791.493265
Median Absolute Deviation (MAD)32
Skewness11.21922392
Sum160717608
Variance53796718.04
MonotocityNot monotonic
2020-09-26T20:22:32.634169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
15851276425.5%
 
531942118.8%
 
156931746.3%
 
1650929175.8%
 
153727355.5%
 
807523404.7%
 
72114612.9%
 
15627711.5%
 
15637171.4%
 
15567001.4%
 
Other values (167)538910.8%
 
(Missing)761115.2%
 
ValueCountFrequency (%) 
711< 0.1%
 
109320.1%
 
1371< 0.1%
 
1744< 0.1%
 
2095< 0.1%
 
ValueCountFrequency (%) 
3943531< 0.1%
 
3934231< 0.1%
 
2010813< 0.1%
 
1353631< 0.1%
 
634731< 0.1%
 

ASN_SRC
Real number (ℝ≥0)

MISSING

Distinct36
Distinct (%)0.1%
Missing12658
Missing (%)25.3%
Infinite0
Infinite (%)0.0%
Mean1260.891115
Minimum149
Maximum27153
Zeros0
Zeros (%)0.0%
Memory size390.6 KiB
2020-09-26T20:22:32.792745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum149
5-th percentile531
Q1531
median1563
Q31580
95-th percentile1585
Maximum27153
Range27004
Interquartile range (IQR)1049

Descriptive statistics

Standard deviation631.3074425
Coefficient of variation (CV)0.5006835524
Kurtosis666.8350051
Mean1260.891115
Median Absolute Deviation (MAD)22
Skewness16.17365691
Sum47084196
Variance398549.0869
MonotocityNot monotonic
2020-09-26T20:22:32.929410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%) 
5311077621.6%
 
1585577311.5%
 
1580570311.4%
 
156339377.9%
 
155621414.3%
 
156512562.5%
 
155912422.5%
 
156611292.3%
 
158610292.1%
 
15699852.0%
 
Other values (26)33716.7%
 
(Missing)1265825.3%
 
ValueCountFrequency (%) 
1493< 0.1%
 
3672< 0.1%
 
3868< 0.1%
 
493860.2%
 
4952060.4%
 
ValueCountFrequency (%) 
271532< 0.1%
 
271421< 0.1%
 
270652< 0.1%
 
270643< 0.1%
 
263471< 0.1%
 

COCOM_DST
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7305
Missing (%)14.6%
Memory size390.6 KiB
USEUCOM
31912 
USNORTHCOM
10723 
USINDOPACOM
 
49
USSOUTHCOM
 
7
USCENTCOM
 
4
ValueCountFrequency (%) 
USEUCOM3191263.8%
 
USNORTHCOM1072321.4%
 
USINDOPACOM490.1%
 
USSOUTHCOM7< 0.1%
 
USCENTCOM4< 0.1%
 
(Missing)730514.6%
 
2020-09-26T20:22:33.073601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-26T20:22:33.167351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:22:33.324929image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length7
Mean length7.06348
Min length3

COCOM_SRC
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing11210
Missing (%)22.4%
Memory size390.6 KiB
USEUCOM
37897 
USNORTHCOM
 
891
USINDOPACOM
 
2
ValueCountFrequency (%) 
USEUCOM3789775.8%
 
USNORTHCOM8911.8%
 
USINDOPACOM2< 0.1%
 
(Missing)1121022.4%
 
2020-09-26T20:22:33.471537image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-26T20:22:33.569309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:22:33.694939image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length7
Mean length6.15682
Min length3

CONNECTIONDETAIL
Categorical

HIGH CARDINALITY
MISSING

Distinct773
Distinct (%)1.8%
Missing6920
Missing (%)13.8%
Memory size390.6 KiB
Dd
15676 
ShADadFf
3010 
D
2243 
Sr
1897 
S
 
1825
Other values (768)
18429 
ValueCountFrequency (%) 
Dd1567631.4%
 
ShADadFf30106.0%
 
D22434.5%
 
Sr18973.8%
 
S18253.6%
 
ShADadFr16733.3%
 
ShADdaFf16233.2%
 
ShAaDdFf11412.3%
 
ShADdFf10472.1%
 
ShADadtFf8691.7%
 
Other values (763)1207624.2%
 
(Missing)692013.8%
 
2020-09-26T20:22:33.878448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique383 ?
Unique (%)0.9%
2020-09-26T20:22:34.051498image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length31
Median length3
Mean length4.245
Min length1

CONNECTIONORIGIN_SRC
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing6424
Missing (%)12.8%
Memory size390.6 KiB
False
38804 
True
4772 
(Missing)
6424 
ValueCountFrequency (%) 
False3880477.6%
 
True47729.5%
 
(Missing)642412.8%
 
2020-09-26T20:22:34.145248image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

CONNECTIONSTATE_BRO
Categorical

MISSING

Distinct13
Distinct (%)< 0.1%
Missing6424
Missing (%)12.8%
Memory size390.6 KiB
SF
27701 
S0
4094 
RSTR
3725 
REJ
 
1903
RSTO
 
1871
Other values (8)
4282 
ValueCountFrequency (%) 
SF2770155.4%
 
S040948.2%
 
RSTR37257.4%
 
REJ19033.8%
 
RSTO18713.7%
 
SH18063.6%
 
OTH15703.1%
 
RSTOS03500.7%
 
SHR2910.6%
 
S11590.3%
 
Other values (3)1060.2%
 
(Missing)642412.8%
 
2020-09-26T20:22:34.265959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-26T20:22:34.455418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.45644
Min length2

COUNTRY_DST
Categorical

MISSING

Distinct24
Distinct (%)0.1%
Missing7299
Missing (%)14.6%
Memory size390.6 KiB
DE
30263 
US
10696 
IE
 
672
NL
 
662
GB
 
276
Other values (19)
 
132
ValueCountFrequency (%) 
DE3026360.5%
 
US1069621.4%
 
IE6721.3%
 
NL6621.3%
 
GB2760.6%
 
CA330.1%
 
KR11< 0.1%
 
CN11< 0.1%
 
AU10< 0.1%
 
LU10< 0.1%
 
Other values (14)570.1%
 
(Missing)729914.6%
 
2020-09-26T20:22:34.705748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique4 ?
Unique (%)< 0.1%
2020-09-26T20:22:34.869310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length2
Mean length2.14598
Min length2

COUNTRY_SRC
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing11210
Missing (%)22.4%
Memory size390.6 KiB
DE
37271 
US
 
891
SM
 
625
JP
 
2
LU
 
1
ValueCountFrequency (%) 
DE3727174.5%
 
US8911.8%
 
SM6251.2%
 
JP2< 0.1%
 
LU1< 0.1%
 
(Missing)1121022.4%
 
2020-09-26T20:22:35.032873image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-09-26T20:22:35.139619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:22:35.295205image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length2
Mean length2.2242
Min length2

COUNT_BYTES_IN
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct4862
Distinct (%)12.1%
Missing9809
Missing (%)19.6%
Infinite0
Infinite (%)0.0%
Mean10767.78978
Minimum0
Maximum69236699
Zeros7119
Zeros (%)14.2%
Memory size390.6 KiB
2020-09-26T20:22:35.440821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q148
median161
Q31553
95-th percentile13806.5
Maximum69236699
Range69236699
Interquartile range (IQR)1505

Descriptive statistics

Standard deviation387608.0455
Coefficient of variation (CV)35.99699228
Kurtosis25521.838
Mean10767.78978
Median Absolute Deviation (MAD)161
Skewness147.4936546
Sum432768239
Variance1.502399969e+11
MonotocityNot monotonic
2020-09-26T20:22:35.602352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0711914.2%
 
4832206.4%
 
155214032.8%
 
155311662.3%
 
567491.5%
 
1737211.4%
 
655301.1%
 
30075021.0%
 
1164130.8%
 
1473980.8%
 
Other values (4852)2397047.9%
 
(Missing)980919.6%
 
ValueCountFrequency (%) 
0711914.2%
 
13< 0.1%
 
41< 0.1%
 
7900.2%
 
81< 0.1%
 
ValueCountFrequency (%) 
692366991< 0.1%
 
169119661< 0.1%
 
158047671< 0.1%
 
119119281< 0.1%
 
104654451< 0.1%
 

COUNT_BYTES_IN_ONTHEWIRE
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct5927
Distinct (%)13.6%
Missing6424
Missing (%)12.8%
Infinite0
Infinite (%)0.0%
Mean11162.67631
Minimum0
Maximum72074863
Zeros7121
Zeros (%)14.2%
Memory size390.6 KiB
2020-09-26T20:22:35.789853image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q176
median177
Q31924
95-th percentile14956
Maximum72074863
Range72074863
Interquartile range (IQR)1848

Descriptive statistics

Standard deviation416161.3393
Coefficient of variation (CV)37.281502
Kurtosis21326.62828
Mean11162.67631
Median Absolute Deviation (MAD)177
Skewness132.3513756
Sum486424783
Variance1.731902603e+11
MonotocityNot monotonic
2020-09-26T20:22:35.939453image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0712114.2%
 
7632246.4%
 
4022464.5%
 
192410552.1%
 
847601.5%
 
19257111.4%
 
935341.1%
 
1444360.9%
 
1703800.8%
 
1183470.7%
 
Other values (5917)2676253.5%
 
(Missing)642412.8%
 
ValueCountFrequency (%) 
0712114.2%
 
291< 0.1%
 
361< 0.1%
 
4022464.5%
 
44260.1%
 
ValueCountFrequency (%) 
720748631< 0.1%
 
277576561< 0.1%
 
185019921< 0.1%
 
170548431< 0.1%
 
163541141< 0.1%
 

COUNT_BYTES_MISSING
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct412
Distinct (%)0.9%
Missing6424
Missing (%)12.8%
Infinite0
Infinite (%)0.0%
Mean591.0926427
Minimum0
Maximum2162121
Zeros42079
Zeros (%)84.2%
Memory size390.6 KiB
2020-09-26T20:22:36.116977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2162121
Range2162121
Interquartile range (IQR)0

Descriptive statistics

Standard deviation25497.9814
Coefficient of variation (CV)43.13703057
Kurtosis5990.284187
Mean591.0926427
Median Absolute Deviation (MAD)0
Skewness74.33494996
Sum25757453
Variance650147055.6
MonotocityNot monotonic
2020-09-26T20:22:36.264582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
04207984.2%
 
14605051.0%
 
5840950.2%
 
4380870.2%
 
2920640.1%
 
7228580.1%
 
7300280.1%
 
12623< 0.1%
 
119< 0.1%
 
868817< 0.1%
 
Other values (402)6011.2%
 
(Missing)642412.8%
 
ValueCountFrequency (%) 
04207984.2%
 
119< 0.1%
 
21< 0.1%
 
31< 0.1%
 
613< 0.1%
 
ValueCountFrequency (%) 
21621211< 0.1%
 
21502731< 0.1%
 
21495691< 0.1%
 
21491201< 0.1%
 
21476801< 0.1%
 

COUNT_BYTES_OUT
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct4076
Distinct (%)10.1%
Missing9809
Missing (%)19.6%
Infinite0
Infinite (%)0.0%
Mean21767.20865
Minimum0
Maximum693224606
Zeros3820
Zeros (%)7.6%
Memory size390.6 KiB
2020-09-26T20:22:36.421164image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q148
median180
Q31177
95-th percentile4393
Maximum693224606
Range693224606
Interquartile range (IQR)1129

Descriptive statistics

Standard deviation3473318.227
Coefficient of variation (CV)159.5665426
Kurtosis39482.70332
Mean21767.20865
Median Absolute Deviation (MAD)180
Skewness197.963122
Sum874845883
Variance1.20639395e+13
MonotocityNot monotonic
2020-09-26T20:22:36.617665image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
038207.6%
 
4834226.8%
 
67426105.2%
 
4912842.6%
 
459221.8%
 
388841.8%
 
7348821.8%
 
506751.4%
 
1806181.2%
 
445501.1%
 
Other values (4066)2452449.0%
 
(Missing)980919.6%
 
ValueCountFrequency (%) 
038207.6%
 
1490.1%
 
81< 0.1%
 
127< 0.1%
 
13940.2%
 
ValueCountFrequency (%) 
6932246061< 0.1%
 
631731661< 0.1%
 
154124381< 0.1%
 
58034431< 0.1%
 
48351221< 0.1%
 

COUNT_BYTES_OUT_ONTHEWIRE
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED

Distinct5295
Distinct (%)12.2%
Missing6424
Missing (%)12.8%
Infinite0
Infinite (%)0.0%
Mean4913.511061
Minimum0
Maximum66011526
Zeros259
Zeros (%)0.5%
Memory size390.6 KiB
2020-09-26T20:22:36.813142image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile52
Q176
median231
Q31488
95-th percentile5605.5
Maximum66011526
Range66011526
Interquartile range (IQR)1412

Descriptive statistics

Standard deviation332189.1295
Coefficient of variation (CV)67.60728232
Kurtosis35933.5896
Mean4913.511061
Median Absolute Deviation (MAD)179
Skewness183.7178112
Sum214111158
Variance1.103496177e+11
MonotocityNot monotonic
2020-09-26T20:22:36.975445image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
7637767.6%
 
104623564.7%
 
5216973.4%
 
7713082.6%
 
739241.8%
 
668971.8%
 
787761.6%
 
407361.5%
 
725541.1%
 
1044961.0%
 
Other values (5285)3005660.1%
 
(Missing)642412.8%
 
ValueCountFrequency (%) 
02590.5%
 
281< 0.1%
 
291< 0.1%
 
361< 0.1%
 
407361.5%
 
ValueCountFrequency (%) 
660115261< 0.1%
 
156436901< 0.1%
 
123071281< 0.1%
 
51995111< 0.1%
 
21911591< 0.1%
 

COUNT_PACKETS_DST
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct371
Distinct (%)0.9%
Missing6424
Missing (%)12.8%
Infinite0
Infinite (%)0.0%
Mean15.81402607
Minimum0
Maximum101363
Zeros7121
Zeros (%)14.2%
Memory size390.6 KiB
2020-09-26T20:22:37.160918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q39
95-th percentile23
Maximum101363
Range101363
Interquartile range (IQR)8

Descriptive statistics

Standard deviation526.124962
Coefficient of variation (CV)33.26951402
Kurtosis31714.46968
Mean15.81402607
Median Absolute Deviation (MAD)1
Skewness167.6830686
Sum689112
Variance276807.4757
MonotocityNot monotonic
2020-09-26T20:22:37.325510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
11705034.1%
 
0712114.2%
 
930956.2%
 
1017303.5%
 
413912.8%
 
212592.5%
 
612202.4%
 
711322.3%
 
510662.1%
 
89782.0%
 
Other values (361)753415.1%
 
(Missing)642412.8%
 
ValueCountFrequency (%) 
0712114.2%
 
11705034.1%
 
212592.5%
 
35731.1%
 
413912.8%
 
ValueCountFrequency (%) 
1013631< 0.1%
 
207461< 0.1%
 
185481< 0.1%
 
138851< 0.1%
 
123521< 0.1%
 

COUNT_PACKETS_SRC
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED

Distinct355
Distinct (%)0.8%
Missing6424
Missing (%)12.8%
Infinite0
Infinite (%)0.0%
Mean14.48467046
Minimum0
Maximum101370
Zeros259
Zeros (%)0.5%
Memory size390.6 KiB
2020-09-26T20:22:37.487085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q39
95-th percentile24
Maximum101370
Range101370
Interquartile range (IQR)8

Descriptive statistics

Standard deviation504.9023609
Coefficient of variation (CV)34.85770438
Kurtosis37308.79434
Mean14.48467046
Median Absolute Deviation (MAD)1
Skewness187.0908029
Sum631184
Variance254926.3941
MonotocityNot monotonic
2020-09-26T20:22:37.644624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
11982739.7%
 
936447.3%
 
1019623.9%
 
219183.8%
 
817713.5%
 
715793.2%
 
614752.9%
 
1114112.8%
 
413842.8%
 
312572.5%
 
Other values (345)734814.7%
 
(Missing)642412.8%
 
ValueCountFrequency (%) 
02590.5%
 
11982739.7%
 
219183.8%
 
312572.5%
 
413842.8%
 
ValueCountFrequency (%) 
1013701< 0.1%
 
174961< 0.1%
 
98251< 0.1%
 
82591< 0.1%
 
69451< 0.1%
 

DURATION
Real number (ℝ≥0)

MISSING
SKEWED

Distinct31048
Distinct (%)77.3%
Missing9809
Missing (%)19.6%
Infinite0
Infinite (%)0.0%
Mean31.23925981
Minimum1e-06
Maximum300819.7988
Zeros0
Zeros (%)0.0%
Memory size390.6 KiB
2020-09-26T20:22:37.814208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1e-06
5-th percentile0.000229
Q10.004461
median0.077013
Q30.753939
95-th percentile60.109311
Maximum300819.7988
Range300819.7988
Interquartile range (IQR)0.749478

Descriptive statistics

Standard deviation1733.392464
Coefficient of variation (CV)55.48762919
Kurtosis23951.28047
Mean31.23925981
Median Absolute Deviation (MAD)0.076232
Skewness147.1346037
Sum1255537.091
Variance3004649.435
MonotocityNot monotonic
2020-09-26T20:22:37.963771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.000206390.1%
 
0.000189350.1%
 
0.000175350.1%
 
0.000204350.1%
 
0.000193340.1%
 
0.000207330.1%
 
0.000213330.1%
 
0.000234330.1%
 
0.000191320.1%
 
0.000192310.1%
 
Other values (31038)3985179.7%
 
(Missing)980919.6%
 
ValueCountFrequency (%) 
1e-061< 0.1%
 
2e-061< 0.1%
 
3e-0612< 0.1%
 
4e-0615< 0.1%
 
5e-0623< 0.1%
 
ValueCountFrequency (%) 
300819.79881< 0.1%
 
149135.74211< 0.1%
 
43158.690521< 0.1%
 
41980.414671< 0.1%
 
41096.853321< 0.1%
 

DURATION_LOG
Real number (ℝ≥0)

MISSING

Distinct5
Distinct (%)100.0%
Missing49995
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean900.0000856
Minimum900.000001
Maximum900.000145
Zeros0
Zeros (%)0.0%
Memory size390.6 KiB
2020-09-26T20:22:38.136308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum900.000001
5-th percentile900.0000164
Q1900.000078
median900.000093
Q3900.000111
95-th percentile900.0001382
Maximum900.000145
Range0.000144
Interquartile range (IQR)3.300000003e-05

Descriptive statistics

Standard deviation5.348644687e-05
Coefficient of variation (CV)5.942937976e-08
Kurtosis0
Mean900.0000856
Median Absolute Deviation (MAD)1.799999995e-05
Skewness-1.033559485
Sum4500.000428
Variance2.860799999e-09
MonotocityNot monotonic
2020-09-26T20:22:38.258980image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
900.0000011< 0.1%
 
900.0001111< 0.1%
 
900.0000931< 0.1%
 
900.0000781< 0.1%
 
900.0001451< 0.1%
 
(Missing)49995> 99.9%
 
ValueCountFrequency (%) 
900.0000011< 0.1%
 
900.0000781< 0.1%
 
900.0000931< 0.1%
 
900.0001111< 0.1%
 
900.0001451< 0.1%
 
ValueCountFrequency (%) 
900.0001451< 0.1%
 
900.0001111< 0.1%
 
900.0000931< 0.1%
 
900.0000781< 0.1%
 
900.0000011< 0.1%
 

FILENAME_INGEST
Categorical

HIGH CARDINALITY

Distinct5454
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Memory size390.6 KiB
bro-e__usareur-tlaw-nj__usareur-tlaw-nj.2020-08-08-0015.bro-conn.369ed.gz
 
98
bro-e__usareur-tlaw-nj__usareur-tlaw-nj.2020-08-08-0945.bro-conn.801f6.gz
 
85
bro-e__usareur-tlaw-nj__usareur-tlaw-nj.2020-08-07-2230.bro-conn.a10ae.gz
 
84
bro-e__usareur-tlas-nj__usareur-tlas-nj.2020-08-02-0015.bro-conn.3f1d1.gz
 
83
bro-e__usareur-apck-nj__usareur-apck-nj.2020-08-07-2230.bro-conn.c342f.gz
 
82
Other values (5449)
49568 
ValueCountFrequency (%) 
bro-e__usareur-tlaw-nj__usareur-tlaw-nj.2020-08-08-0015.bro-conn.369ed.gz980.2%
 
bro-e__usareur-tlaw-nj__usareur-tlaw-nj.2020-08-08-0945.bro-conn.801f6.gz850.2%
 
bro-e__usareur-tlaw-nj__usareur-tlaw-nj.2020-08-07-2230.bro-conn.a10ae.gz840.2%
 
bro-e__usareur-tlas-nj__usareur-tlas-nj.2020-08-02-0015.bro-conn.3f1d1.gz830.2%
 
bro-e__usareur-apck-nj__usareur-apck-nj.2020-08-07-2230.bro-conn.c342f.gz820.2%
 
bro-e__usareur-tlaw-nj__usareur-tlaw-nj.2020-08-07-0745.bro-conn.608c7.gz810.2%
 
bro-e__usareur-tlaw-nj__usareur-tlaw-nj.2020-08-02-0015.bro-conn.3087c.gz810.2%
 
bro-e__usareur-tlaw-nj__usareur-tlaw-nj.2020-08-08-0930.bro-conn.ec507.gz770.2%
 
bro-e__usareur-tlaw-nj__usareur-tlaw-nj.2020-08-08-0615.bro-conn.c406a.gz760.2%
 
bro-e__usareur-apck-nj__usareur-apck-nj.2020-08-08-1700.bro-conn.56cd3.gz760.2%
 
Other values (5444)4917798.4%
 
2020-09-26T20:22:38.461440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1579 ?
Unique (%)3.2%
2020-09-26T20:22:38.645945image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length84
Median length73
Mean length74.06198
Min length63

IPBRANCHCATEGORY_DST
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing7112
Missing (%)14.2%
Memory size390.6 KiB
Army
33474 
Non-Army
9414 
ValueCountFrequency (%) 
Army3347466.9%
 
Non-Army941418.8%
 
(Missing)711214.2%
 
2020-09-26T20:22:38.777628image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-26T20:22:38.856381image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:22:38.954119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length4
Mean length4.61088
Min length3

IPBRANCHCATEGORY_SRC
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing11210
Missing (%)22.4%
Memory size390.6 KiB
Army
38435 
Non-Army
 
355
ValueCountFrequency (%) 
Army3843576.9%
 
Non-Army3550.7%
 
(Missing)1121022.4%
 
2020-09-26T20:22:39.092786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-26T20:22:39.190488image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:22:39.294212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length4
Mean length3.8042
Min length3

IP_DST
Categorical

HIGH CARDINALITY
MISSING

Distinct4521
Distinct (%)10.4%
Missing6424
Missing (%)12.8%
Memory size390.6 KiB
52.11.247.82
 
2611
155.155.72.10
 
2530
139.139.3.146
 
2510
155.155.72.9
 
1895
136.215.193.9
 
1778
Other values (4516)
32252 
ValueCountFrequency (%) 
52.11.247.8226115.2%
 
155.155.72.1025305.1%
 
139.139.3.14625105.0%
 
155.155.72.918953.8%
 
136.215.193.917783.6%
 
136.215.178.12016873.4%
 
136.215.78.12016533.3%
 
134.232.201.914783.0%
 
164.169.0.613692.7%
 
164.169.0.2213662.7%
 
Other values (4511)2469949.4%
 
(Missing)642412.8%
 
2020-09-26T20:22:39.510616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3341 ?
Unique (%)7.7%
2020-09-26T20:22:39.716066image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length15
Median length13
Mean length11.77816
Min length3

IP_SRC
Categorical

HIGH CARDINALITY
MISSING

Distinct19797
Distinct (%)45.4%
Missing6424
Missing (%)12.8%
Memory size390.6 KiB
155.155.237.11
 
1401
155.155.72.10
 
495
155.155.72.9
 
448
155.24.116.43
 
415
136.218.18.66
 
380
Other values (19792)
40437 
ValueCountFrequency (%) 
155.155.237.1114012.8%
 
155.155.72.104951.0%
 
155.155.72.94480.9%
 
155.24.116.434150.8%
 
136.218.18.663800.8%
 
136.215.64.453620.7%
 
147.35.17.343350.7%
 
136.215.64.433070.6%
 
136.215.177.1372790.6%
 
147.36.253.822680.5%
 
Other values (19787)3888677.8%
 
(Missing)642412.8%
 
2020-09-26T20:22:39.942998image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique12849 ?
Unique (%)29.5%
2020-09-26T20:22:40.130611image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length25
Median length13
Mean length11.94806
Min length3

LATITUDE_DST
Real number (ℝ)

MISSING

Distinct127
Distinct (%)0.3%
Missing7253
Missing (%)14.5%
Infinite0
Infinite (%)0.0%
Mean47.21507066
Minimum-33.5318
Maximum59.95
Zeros0
Zeros (%)0.0%
Memory size390.6 KiB
2020-09-26T20:22:40.284197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-33.5318
5-th percentile33.41516
Q145.8491
median50.084075
Q350.084075
95-th percentile50.084075
Maximum59.95
Range93.4818
Interquartile range (IQR)4.234975

Descriptive statistics

Standard deviation5.923323035
Coefficient of variation (CV)0.1254540754
Kurtosis14.59508794
Mean47.21507066
Median Absolute Deviation (MAD)0
Skewness-2.463274226
Sum2018302.625
Variance35.08575578
MonotocityNot monotonic
2020-09-26T20:22:40.442776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
50.0840753023060.5%
 
37.75131406.3%
 
33.4151627395.5%
 
45.849126265.3%
 
53.33386601.3%
 
36.65345261.1%
 
52.37594630.9%
 
38.70952510.5%
 
31.5678242310.5%
 
37.33882000.4%
 
Other values (117)16813.4%
 
(Missing)725314.5%
 
ValueCountFrequency (%) 
-33.53181< 0.1%
 
-33.4949< 0.1%
 
-337< 0.1%
 
-6.1754< 0.1%
 
21.345856< 0.1%
 
ValueCountFrequency (%) 
59.951< 0.1%
 
59.94521< 0.1%
 
59.32476< 0.1%
 
54.56735< 0.1%
 
54.56671< 0.1%
 

LATITUDE_SRC
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct23
Distinct (%)0.1%
Missing11210
Missing (%)22.4%
Infinite0
Infinite (%)0.0%
Mean49.70427453
Minimum29.460896
Maximum50.084075
Zeros0
Zeros (%)0.0%
Memory size390.6 KiB
2020-09-26T20:22:40.589351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum29.460896
5-th percentile50.084075
Q150.084075
median50.084075
Q350.084075
95-th percentile50.084075
Maximum50.084075
Range20.623179
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.115699411
Coefficient of variation (CV)0.04256574371
Kurtosis41.20322081
Mean49.70427453
Median Absolute Deviation (MAD)0
Skewness-6.274167344
Sum1928028.809
Variance4.476183998
MonotocityNot monotonic
2020-09-26T20:22:40.716012image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%) 
50.0840753727074.5%
 
45.5488416211.2%
 
37.7513470.7%
 
39.4632821680.3%
 
31.997176930.2%
 
39.493088900.2%
 
31.567824690.1%
 
36.628256370.1%
 
31.870154330.1%
 
33.4151619< 0.1%
 
Other values (13)430.1%
 
(Missing)1121022.4%
 
ValueCountFrequency (%) 
29.4608962< 0.1%
 
31.1436427< 0.1%
 
31.5639012< 0.1%
 
31.567824690.1%
 
31.870154330.1%
 
ValueCountFrequency (%) 
50.0840753727074.5%
 
49.5283331< 0.1%
 
48.71921< 0.1%
 
45.572084< 0.1%
 
45.5488416211.2%
 

LOCAL_TIMESTAMP
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct43546
Distinct (%)> 99.9%
Missing6448
Missing (%)12.9%
Memory size390.6 KiB
2020-08-07T15:07:07.731+02:00[Europe/Berlin]
 
2
2020-08-08T23:35:21.110+02:00[Europe/Berlin]
 
2
2020-08-08T15:26:43.244+02:00[Europe/Berlin]
 
2
2020-08-08T17:08:22.287+02:00[Europe/Berlin]
 
2
2020-08-02T13:33:03.821+02:00[Europe/Berlin]
 
2
Other values (43541)
43542 
ValueCountFrequency (%) 
2020-08-07T15:07:07.731+02:00[Europe/Berlin]2< 0.1%
 
2020-08-08T23:35:21.110+02:00[Europe/Berlin]2< 0.1%
 
2020-08-08T15:26:43.244+02:00[Europe/Berlin]2< 0.1%
 
2020-08-08T17:08:22.287+02:00[Europe/Berlin]2< 0.1%
 
2020-08-02T13:33:03.821+02:00[Europe/Berlin]2< 0.1%
 
2020-08-08T01:29:21.716+02:00[Europe/Berlin]2< 0.1%
 
2020-08-02T18:11:12.159+02:00[Europe/Berlin]1< 0.1%
 
2020-08-02T18:45:39.212+02:00[Europe/Berlin]1< 0.1%
 
2020-08-08T20:15:38.566+02:00[Europe/Rome]1< 0.1%
 
2020-08-07T02:47:07.177+02:00[Europe/Berlin]1< 0.1%
 
Other values (43536)4353687.1%
 
(Missing)644812.9%
 
2020-09-26T20:22:40.992789image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique43540 ?
Unique (%)> 99.9%
2020-09-26T20:22:41.156354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length50
Median length44
Mean length38.7329
Min length3

LONGITUDE_DST
Real number (ℝ)

MISSING

Distinct128
Distinct (%)0.3%
Missing7253
Missing (%)14.5%
Infinite0
Infinite (%)0.0%
Mean-18.60561574
Minimum-157.886471
Maximum150.7761
Zeros4
Zeros (%)< 0.1%
Memory size390.6 KiB
2020-09-26T20:22:41.307946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-157.886471
5-th percentile-119.7143
Q1-76.120398
median8.25384
Q38.25384
95-th percentile8.25384
Maximum150.7761
Range308.662571
Interquartile range (IQR)84.374238

Descriptive statistics

Standard deviation46.82575941
Coefficient of variation (CV)-2.516754085
Kurtosis-0.222193917
Mean-18.60561574
Median Absolute Deviation (MAD)0
Skewness-1.1977836
Sum-795334.2558
Variance2192.651744
MonotocityNot monotonic
2020-09-26T20:22:41.461535image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
8.253843023060.5%
 
-97.82231406.3%
 
-82.14257327395.5%
 
-119.714326265.3%
 
-6.24886601.3%
 
-78.3755261.1%
 
4.89754630.9%
 
-78.15392510.5%
 
-110.3768172310.5%
 
-121.89142000.4%
 
Other values (118)16813.4%
 
(Missing)725314.5%
 
ValueCountFrequency (%) 
-157.8864716< 0.1%
 
-123.03861< 0.1%
 
-122.49467< 0.1%
 
-122.34517< 0.1%
 
-122.3412630.1%
 
ValueCountFrequency (%) 
150.77611< 0.1%
 
143.21049< 0.1%
 
139.697< 0.1%
 
127.1121219< 0.1%
 
126.97831< 0.1%
 

LONGITUDE_SRC
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct23
Distinct (%)0.1%
Missing11210
Missing (%)22.4%
Infinite0
Infinite (%)0.0%
Mean6.080586965
Minimum-110.376817
Maximum139.69
Zeros0
Zeros (%)0.0%
Memory size390.6 KiB
2020-09-26T20:22:41.615125image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-110.376817
5-th percentile8.25384
Q18.25384
median8.25384
Q38.25384
95-th percentile8.25384
Maximum139.69
Range250.066817
Interquartile range (IQR)0

Descriptive statistics

Standard deviation14.71014307
Coefficient of variation (CV)2.419197877
Kurtosis40.8988417
Mean6.080586965
Median Absolute Deviation (MAD)0
Skewness-6.421194996
Sum235865.9684
Variance216.3883093
MonotocityNot monotonic
2020-09-26T20:22:41.750763image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%) 
8.253843727074.5%
 
11.5374286211.2%
 
-97.8223470.7%
 
-76.1203981680.3%
 
-81.230724930.2%
 
-76.172541900.2%
 
-110.376817690.1%
 
-87.462866370.1%
 
-81.631076330.1%
 
-82.14257319< 0.1%
 
Other values (13)430.1%
 
(Missing)1121022.4%
 
ValueCountFrequency (%) 
-110.376817690.1%
 
-110.3495552< 0.1%
 
-106.65832< 0.1%
 
-98.438342< 0.1%
 
-97.8223470.7%
 
ValueCountFrequency (%) 
139.692< 0.1%
 
11.5374286211.2%
 
11.526694< 0.1%
 
8.9611< 0.1%
 
8.253843727074.5%
 

MESSAGE
Categorical

HIGH CARDINALITY
MISSING

Distinct4260
Distinct (%)66.4%
Missing43581
Missing (%)87.2%
Memory size390.6 KiB
child statistics: [1] pending=0 bytes=0K/0K chunks=10/9 io=4/5 bytes/io=0.06K/0.05K
 
311
child statistics: [0] pending=0 bytes=0K/0K chunks=10/9 io=3/5 bytes/io=0.08K/0.05K
 
225
child statistics: [1] pending=0 bytes=0K/0K chunks=10/9 io=5/5 bytes/io=0.05K/0.05K
 
169
child statistics: [3] pending=0 bytes=0K/0K chunks=9/10 io=3/6 bytes/io=0.08K/0.04K
 
139
child statistics: [1] pending=0 bytes=0K/0K chunks=9/10 io=4/6 bytes/io=0.06K/0.04K
 
128
Other values (4255)
5447 
ValueCountFrequency (%) 
child statistics: [1] pending=0 bytes=0K/0K chunks=10/9 io=4/5 bytes/io=0.06K/0.05K3110.6%
 
child statistics: [0] pending=0 bytes=0K/0K chunks=10/9 io=3/5 bytes/io=0.08K/0.05K2250.4%
 
child statistics: [1] pending=0 bytes=0K/0K chunks=10/9 io=5/5 bytes/io=0.05K/0.05K1690.3%
 
child statistics: [3] pending=0 bytes=0K/0K chunks=9/10 io=3/6 bytes/io=0.08K/0.04K1390.3%
 
child statistics: [1] pending=0 bytes=0K/0K chunks=9/10 io=4/6 bytes/io=0.06K/0.04K1280.3%
 
child statistics: [2] pending=0 bytes=0K/0K chunks=9/10 io=3/6 bytes/io=0.08K/0.04K1050.2%
 
child statistics: [0] pending=0 bytes=0K/0K chunks=10/9 io=5/5 bytes/io=0.05K/0.05K1040.2%
 
child statistics: [1] pending=0 bytes=0K/0K chunks=10/9 io=3/5 bytes/io=0.08K/0.05K860.2%
 
child statistics: [3] pending=0 bytes=0K/0K chunks=9/10 io=4/6 bytes/io=0.06K/0.04K840.2%
 
child statistics: [2] pending=0 bytes=0K/0K chunks=9/10 io=4/6 bytes/io=0.06K/0.04K800.2%
 
Other values (4250)498810.0%
 
(Missing)4358187.2%
 
2020-09-26T20:22:41.958207image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique4229 ?
Unique (%)65.9%
2020-09-26T20:22:42.579580image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length175
Median length3
Mean length17.11254
Min length3

MSGSOURCE_BRO
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing43581
Missing (%)87.2%
Memory size390.6 KiB
parent
6374 
child
 
45
ValueCountFrequency (%) 
parent637412.7%
 
child450.1%
 
(Missing)4358187.2%
 
2020-09-26T20:22:42.736202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-26T20:22:42.823009image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:22:42.921978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length3
Mean length3.38424
Min length3

NUMPACKETS_ACK
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)100.0%
Missing49995
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean2755913.8
Minimum1584139
Maximum5142184
Zeros0
Zeros (%)0.0%
Memory size390.6 KiB
2020-09-26T20:22:43.040307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1584139
5-th percentile1666657.6
Q11996732
median2225841
Q32830673
95-th percentile4679881.8
Maximum5142184
Range3558045
Interquartile range (IQR)833941

Descriptive statistics

Standard deviation1408036.289
Coefficient of variation (CV)0.5109144884
Kurtosis3.042084392
Mean2755913.8
Median Absolute Deviation (MAD)604832
Skewness1.700550509
Sum13779569
Variance1.982566192e+12
MonotocityNot monotonic
2020-09-26T20:22:43.156770image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
51421841< 0.1%
 
28306731< 0.1%
 
22258411< 0.1%
 
19967321< 0.1%
 
15841391< 0.1%
 
(Missing)49995> 99.9%
 
ValueCountFrequency (%) 
15841391< 0.1%
 
19967321< 0.1%
 
22258411< 0.1%
 
28306731< 0.1%
 
51421841< 0.1%
 
ValueCountFrequency (%) 
51421841< 0.1%
 
28306731< 0.1%
 
22258411< 0.1%
 
19967321< 0.1%
 
15841391< 0.1%
 

NUMPACKETS_LOSS
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)40.0%
Missing49995
Missing (%)> 99.9%
Memory size390.6 KiB
0
 
4
1
 
1
(Missing)
49995 
ValueCountFrequency (%) 
04< 0.1%
 
11< 0.1%
 
(Missing)49995> 99.9%
 
2020-09-26T20:22:43.265108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

NUMPACKETS_LOSS_PERCENT
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)40.0%
Missing49995
Missing (%)> 99.9%
Memory size390.6 KiB
0
 
4
1.9e-05
 
1
(Missing)
49995 
ValueCountFrequency (%) 
04< 0.1%
 
1.9e-051< 0.1%
 
(Missing)49995> 99.9%
 
2020-09-26T20:22:43.322919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

ORGANIZATION_OWNER_DST
Categorical

MISSING

Distinct36
Distinct (%)0.1%
Missing16526
Missing (%)33.1%
Memory size390.6 KiB
HQ, 5TH SIGNAL COMMAND
30230 
HQ, 7TH SIGNAL COMMAND
 
2817
US ARMY NETWORK ENTERPRISE TECHNOLOGY COMMAND
 
145
PEO-C3T
 
58
USA SIGNAL NETWORK ENTERPRISE CNTR-FORT BELVOIR
 
19
Other values (31)
 
205
ValueCountFrequency (%) 
HQ, 5TH SIGNAL COMMAND3023060.5%
 
HQ, 7TH SIGNAL COMMAND28175.6%
 
US ARMY NETWORK ENTERPRISE TECHNOLOGY COMMAND1450.3%
 
PEO-C3T580.1%
 
USA SIGNAL NETWORK ENTERPRISE CNTR-FORT BELVOIR19< 0.1%
 
NEC FORT MEADE17< 0.1%
 
RNEC-APG16< 0.1%
 
FORT SAM HOUSTON16< 0.1%
 
PROGRAM EXECUTIVE OFFICE FOR COMMAND, CONTROL16< 0.1%
 
US ARMY CRIMINAL INVESTIGATION COMMAND14< 0.1%
 
Other values (26)1260.3%
 
(Missing)1652633.1%
 
2020-09-26T20:22:43.457681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique5 ?
Unique (%)< 0.1%
2020-09-26T20:22:43.616256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length47
Median length22
Mean length15.7886
Min length3

ORGANIZATION_OWNER_SRC
Categorical

MISSING

Distinct18
Distinct (%)< 0.1%
Missing11565
Missing (%)23.1%
Memory size390.6 KiB
HQ, 5TH SIGNAL COMMAND
37270 
173RD AIRBORNE BRIGADE
 
621
PEO-C3T
 
168
3RD COMBAT AVIATION BDE, 3RD INF DIV
 
93
PROGRAM EXECUTIVE OFFICE FOR COMMAND, CONTROL
 
90
Other values (13)
 
193
ValueCountFrequency (%) 
HQ, 5TH SIGNAL COMMAND3727074.5%
 
173RD AIRBORNE BRIGADE6211.2%
 
PEO-C3T1680.3%
 
3RD COMBAT AVIATION BDE, 3RD INF DIV930.2%
 
PROGRAM EXECUTIVE OFFICE FOR COMMAND, CONTROL900.2%
 
HQ, 7TH SIGNAL COMMAND870.2%
 
101ST COMBAT AVIATION BRIGADE370.1%
 
2ND BRIGADE COMBAT TEAM, 3RD INFANTRY DIVISION330.1%
 
US ARMY INSPECTOR GENERAL AGENCY10< 0.1%
 
1ST CAVALRY DIVISION7< 0.1%
 
Other values (8)19< 0.1%
 
(Missing)1156523.1%
 
2020-09-26T20:22:43.774833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3 ?
Unique (%)< 0.1%
2020-09-26T20:22:44.166529image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length46
Median length22
Mean length17.64272
Min length3

PEERNAME
Categorical

MISSING

Distinct8
Distinct (%)0.1%
Missing43576
Missing (%)87.2%
Memory size390.6 KiB
manager
1624 
proxy-1
1602 
worker-1-1
775 
worker-1-2
749 
worker-1-3
722 
Other values (3)
952 
ValueCountFrequency (%) 
manager16243.2%
 
proxy-116023.2%
 
worker-1-17751.6%
 
worker-1-27491.5%
 
worker-1-37221.4%
 
worker-1-47021.4%
 
worker-1-51340.3%
 
worker-1-61160.2%
 
(Missing)4357687.2%
 
2020-09-26T20:22:44.541523image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-26T20:22:44.763767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:22:45.055164image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length3
Mean length3.7058
Min length3

PORT_DST
Real number (ℝ≥0)

MISSING

Distinct2561
Distinct (%)5.9%
Missing6424
Missing (%)12.8%
Infinite0
Infinite (%)0.0%
Mean5428.622636
Minimum0
Maximum65534
Zeros334
Zeros (%)0.7%
Memory size390.6 KiB
2020-09-26T20:22:45.246651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile53
Q153
median389
Q3500
95-th percentile50514.5
Maximum65534
Range65534
Interquartile range (IQR)447

Descriptive statistics

Standard deviation13664.79448
Coefficient of variation (CV)2.517175239
Kurtosis9.355058085
Mean5428.622636
Median Absolute Deviation (MAD)336
Skewness3.187526624
Sum236557660
Variance186726608.2
MonotocityNot monotonic
2020-09-26T20:22:45.404736image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
531171223.4%
 
443867117.3%
 
12336657.3%
 
38929625.9%
 
1747227535.5%
 
44511742.3%
 
8011732.3%
 
1379842.0%
 
91007441.5%
 
84435591.1%
 
Other values (2551)917918.4%
 
(Missing)642412.8%
 
ValueCountFrequency (%) 
03340.7%
 
113< 0.1%
 
22< 0.1%
 
31300.3%
 
43< 0.1%
 
ValueCountFrequency (%) 
655348< 0.1%
 
655241< 0.1%
 
655141< 0.1%
 
654981< 0.1%
 
654891< 0.1%
 

PORT_SRC
Real number (ℝ≥0)

MISSING

Distinct17331
Distinct (%)39.8%
Missing6424
Missing (%)12.8%
Infinite0
Infinite (%)0.0%
Mean45179.4502
Minimum0
Maximum65535
Zeros6
Zeros (%)< 0.1%
Memory size390.6 KiB
2020-09-26T20:22:45.576278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile123
Q144304
median53365
Q359274
95-th percentile64268
Maximum65535
Range65535
Interquartile range (IQR)14970

Descriptive statistics

Standard deviation21575.02823
Coefficient of variation (CV)0.4775407432
Kurtosis0.1696762493
Mean45179.4502
Median Absolute Deviation (MAD)6177.5
Skewness-1.330050899
Sum1968739722
Variance465481843.1
MonotocityNot monotonic
2020-09-26T20:22:45.719412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
12336267.3%
 
598517973.6%
 
1379832.0%
 
83290.7%
 
4432810.6%
 
31510.3%
 
395351120.2%
 
36402630.1%
 
500600.1%
 
52954580.1%
 
Other values (17321)3611672.2%
 
(Missing)642412.8%
 
ValueCountFrequency (%) 
06< 0.1%
 
31510.3%
 
83290.7%
 
116< 0.1%
 
132< 0.1%
 
ValueCountFrequency (%) 
655351< 0.1%
 
655324< 0.1%
 
655304< 0.1%
 
655292< 0.1%
 
655281< 0.1%
 

RESPONSEORIGIN_DST
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing6424
Missing (%)12.8%
Memory size390.6 KiB
False
42979 
True
 
597
(Missing)
6424 
ValueCountFrequency (%) 
False4297986.0%
 
True5971.2%
 
(Missing)642412.8%
 
2020-09-26T20:22:45.826088image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

SEVERITY_MESSAGE
Categorical

MISSING

Distinct1
Distinct (%)< 0.1%
Missing43581
Missing (%)87.2%
Memory size390.6 KiB
info
6419 
ValueCountFrequency (%) 
info641912.8%
 
(Missing)4358187.2%
 
2020-09-26T20:22:45.916846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-26T20:22:46.033049image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:22:46.219614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length3
Mean length3.12838
Min length3

SITE_COLLECTION
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size390.6 KiB
usareur-tlaw-nj
14330 
usareur-apck-nj
12823 
usareur-tlas-nj
9194 
usareur-apcg-nj
5695 
usareur-coi8-nl
2037 
Other values (7)
5921 
ValueCountFrequency (%) 
usareur-tlaw-nj1433028.7%
 
usareur-apck-nj1282325.6%
 
usareur-tlas-nj919418.4%
 
usareur-apcg-nj569511.4%
 
usareur-coi8-nl20374.1%
 
usareur-tlal-nla11732.3%
 
usareur-tlal-nlp11062.2%
 
usareur-coi4-nl9461.9%
 
rss-gcc-t19271.9%
 
usareur-coi3-nl6181.2%
 
Other values (2)11512.3%
 
2020-09-26T20:22:46.400627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-26T20:22:46.568178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length16
Median length15
Mean length14.95288
Min length10

TIME_RECEIPT
Categorical

HIGH CARDINALITY
UNIFORM

Distinct49932
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size390.6 KiB
2020-08-07T14:13:50.764Z
 
2
2020-08-02T17:55:26.122Z
 
2
2020-08-07T13:17:15.04Z
 
2
2020-08-07T09:38:33.319Z
 
2
2020-08-08T02:42:57.495Z
 
2
Other values (49927)
49990 
ValueCountFrequency (%) 
2020-08-07T14:13:50.764Z2< 0.1%
 
2020-08-02T17:55:26.122Z2< 0.1%
 
2020-08-07T13:17:15.04Z2< 0.1%
 
2020-08-07T09:38:33.319Z2< 0.1%
 
2020-08-08T02:42:57.495Z2< 0.1%
 
2020-08-07T17:38:33.648Z2< 0.1%
 
2020-08-08T13:26:43.244Z2< 0.1%
 
2020-08-07T00:37:53.897Z2< 0.1%
 
2020-08-07T04:32:33.285Z2< 0.1%
 
2020-08-08T20:34:29.893Z2< 0.1%
 
Other values (49922)49980> 99.9%
 
2020-09-26T20:22:46.843475image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique49864 ?
Unique (%)99.7%
2020-09-26T20:22:47.009996image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length24
Median length24
Mean length23.88942
Min length20

TRANSPORTPROTOCOL
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing6424
Missing (%)12.8%
Memory size390.6 KiB
tcp
24977 
udp
18103 
icmp
 
496
ValueCountFrequency (%) 
tcp2497750.0%
 
udp1810336.2%
 
icmp4961.0%
 
(Missing)642412.8%
 
2020-09-26T20:22:47.161591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-26T20:22:47.282268image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:22:47.418083image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length3
Mean length3.00992
Min length3

TUNNELPARENTUUIDS
Categorical

HIGH CARDINALITY
MISSING

Distinct150
Distinct (%)18.1%
Missing49171
Missing (%)98.3%
Memory size390.6 KiB
CxfW4t4Rt0M3AfFRe1
 
24
CSblFE4uQ1IVgXSorc
 
24
CuOoXF29ROmzzkyu5k
 
22
CpJs8z3kIw0L1ZWay5
 
22
CM0ydC1RMqcgjgDwAh
 
22
Other values (145)
715 
ValueCountFrequency (%) 
CxfW4t4Rt0M3AfFRe124< 0.1%
 
CSblFE4uQ1IVgXSorc24< 0.1%
 
CuOoXF29ROmzzkyu5k22< 0.1%
 
CpJs8z3kIw0L1ZWay522< 0.1%
 
CM0ydC1RMqcgjgDwAh22< 0.1%
 
ClNMRV1NYjm8fBaGbi21< 0.1%
 
C3Jcrk1LaFlHsoTmBc20< 0.1%
 
CouFAj29LEcppMqjk419< 0.1%
 
C5Y8UyfKfI1rUDEF218< 0.1%
 
CefH9w4HvFwRM4Hkz617< 0.1%
 
Other values (140)6201.2%
 
(Missing)4917198.3%
 
2020-09-26T20:22:47.600629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique59 ?
Unique (%)7.1%
2020-09-26T20:22:47.772135image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length37
Median length3
Mean length3.2466
Min length3

TYPE_BRO
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size390.6 KiB
conn
43576 
communication
6419 
capture_loss
 
5
ValueCountFrequency (%) 
conn4357687.2%
 
communication641912.8%
 
capture_loss5< 0.1%
 
2020-09-26T20:22:47.921737image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-26T20:22:48.049394image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:22:48.216945image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length4
Mean length5.15622
Min length4

UUID_BRO
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct43576
Distinct (%)100.0%
Missing6424
Missing (%)12.8%
Memory size390.6 KiB
C0Gs5pB9UbVMnVKrj
 
1
C03arjKxL6GrFCZpl
 
1
C0J2ZT3wD1Mx5KAIci
 
1
C07f562RP8pPfPhwo1
 
1
C04uNF2L35PgXeEVci
 
1
Other values (43571)
43571 
ValueCountFrequency (%) 
C0Gs5pB9UbVMnVKrj1< 0.1%
 
C03arjKxL6GrFCZpl1< 0.1%
 
C0J2ZT3wD1Mx5KAIci1< 0.1%
 
C07f562RP8pPfPhwo11< 0.1%
 
C04uNF2L35PgXeEVci1< 0.1%
 
C0MZIX1e5CdSes6FP1< 0.1%
 
C00inL2qt4m0RjPs931< 0.1%
 
C086svQU58bCTK2Oe1< 0.1%
 
C0DqAO1Uv7eD4Hqgi61< 0.1%
 
C0UTMo3Q2znmQFaSNj1< 0.1%
 
Other values (43566)4356687.1%
 
(Missing)642412.8%
 
2020-09-26T20:22:48.486224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique43576 ?
Unique (%)100.0%
2020-09-26T20:22:48.648791image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length18
Median length18
Mean length15.84224
Min length3

Interactions

2020-09-26T20:21:26.614019image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:26.731704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:26.837422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:26.940148image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:27.047890image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:27.157597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:27.270262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:27.381964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:27.507627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:27.626346image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:27.739041image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:27.853734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:27.969394image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:28.080096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:28.193791image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:28.309482image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:28.438138image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:28.559847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:28.768254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:29.054489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:29.240990image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:29.424498image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:29.582077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:29.722700image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:29.858337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:29.975631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:30.094282image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:30.210970image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:30.325696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:30.440389image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:30.552057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:30.661761image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:30.783438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:30.903118image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:31.016814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:31.122561image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:31.235262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:31.347925image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:31.456634image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:31.567338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:31.684028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:31.793762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:31.906945image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:32.020673image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:32.136329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:32.246037image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:32.357738image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:32.470468image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:32.590116image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:32.732734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:32.881336image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:33.018969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:33.171559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:33.309192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:33.536617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:33.662247image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:33.830795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:33.986380image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:34.132986image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:34.286575image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:34.459115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:34.627663image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:34.768320image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:34.914926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:35.044582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:35.172206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:35.292914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:35.405615image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:35.521305image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:35.635002image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:35.757638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:35.892703image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:36.004355image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:36.122041image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:36.242750image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:36.355416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:36.476094image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:36.597768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:36.722435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:36.849137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:36.978777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:37.101447image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:37.224087image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:37.337781image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:37.476413image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:37.595130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:37.715769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:37.841464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:37.952680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:38.067377image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:38.186090image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:38.304742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:38.424453image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:38.536121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:38.643865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:38.751544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:38.877210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:38.998883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:39.243229image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:39.396818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:39.542429image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:39.697015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:39.823708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:39.944353image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:40.057086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:40.162768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:40.264496image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:40.362266image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:40.472940image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:40.577691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:40.696372image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:40.808074image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:40.917780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:41.026489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:41.134169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:41.246867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:41.358600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:41.473293image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:41.580975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:41.692709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:41.804375image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:41.921095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:42.043735image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:42.167436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:42.282097image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:42.396822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:42.512512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:42.621222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:42.728933image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:42.839607image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:42.950820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:43.066511image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:43.182201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:43.298921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:43.410658image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:43.534329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:43.641615image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:43.756276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:43.865017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:43.978713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:44.099360image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:44.224027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:44.358664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:21:44.508264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-09-26T20:22:13.165040image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:22:13.345558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:22:13.555996image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:22:13.777412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:22:13.964913image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:22:14.109527image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:22:14.266144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:22:14.427539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:22:14.602073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:22:14.771618image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:22:14.935183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-09-26T20:22:48.802409image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-09-26T20:22:49.220260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-09-26T20:22:49.702969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-09-26T20:22:50.178698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-09-26T20:22:15.948170image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:22:23.066235image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:22:26.029351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-26T20:22:29.599803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

IdTimestampData TypeVisibilityAPPLICATIONPROTOCOLASN_DSTASN_SRCCOCOM_DSTCOCOM_SRCCONNECTIONDETAILCONNECTIONORIGIN_SRCCONNECTIONSTATE_BROCOUNTRY_DSTCOUNTRY_SRCCOUNT_BYTES_INCOUNT_BYTES_IN_ONTHEWIRECOUNT_BYTES_MISSINGCOUNT_BYTES_OUTCOUNT_BYTES_OUT_ONTHEWIRECOUNT_PACKETS_DSTCOUNT_PACKETS_SRCDURATIONDURATION_LOGFILENAME_INGESTIPBRANCHCATEGORY_DSTIPBRANCHCATEGORY_SRCIP_DSTIP_SRCLATITUDE_DSTLATITUDE_SRCLOCAL_TIMESTAMPLONGITUDE_DSTLONGITUDE_SRCMESSAGEMSGSOURCE_BRONUMPACKETS_ACKNUMPACKETS_LOSSNUMPACKETS_LOSS_PERCENTORGANIZATION_OWNER_DSTORGANIZATION_OWNER_SRCPEERNAMEPORT_DSTPORT_SRCRESPONSEORIGIN_DSTSEVERITY_MESSAGESITE_COLLECTIONTIME_RECEIPTTRANSPORTPROTOCOLTUNNELPARENTUUIDSTYPE_BROUUID_BRO
0002eb37302a3731459ff6127495630fb1596892957292bro-e-communicationU&FOUONaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNbro-e__usareur-tlaw-nj__usareur-tlaw-nj.2020-08-08-1330.bro-communication.c5072.gzNaNNaNNaNNaNNaNNaNNaNNaNNaNchild statistics: [1] pending=0 bytes=0K/0K chunks=10/9 io=4/5 bytes/io=0.06K/0.05KparentNaNNaNNaNNaNNaNworker-1-1NaNNaNNaNinfousareur-tlaw-nj2020-08-08T13:22:37.292ZNaNNaNcommunicationNaN
1004d6e692bcf67cde35fa94b1718d4971596885597739bro-e-communicationU&FOUONaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNbro-e__usareur-coi5-nl__usareur-coi5-nl.2020-08-08-1130.bro-communication.0a05d.gzNaNNaNNaNNaNNaNNaNNaNNaNNaNchild statistics: [0] pending=0 bytes=2K/0K chunks=14/9 io=4/5 bytes/io=0.56K/0.05KparentNaNNaNNaNNaNNaNworker-1-3NaNNaNNaNinfousareur-coi5-nl2020-08-08T11:19:57.739ZNaNNaNcommunicationNaN
20062ca060d4cef18f85774a3e46aa60c1596876837734bro-e-communicationU&FOUONaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNbro-e__usareur-coi5-nl__usareur-coi5-nl.2020-08-08-0900.bro-communication.06210.gzNaNNaNNaNNaNNaNNaNNaNNaNNaNchild statistics: [1] pending=0 bytes=706509K/15522273K chunks=7017226/16016519 io=3202948/8008260 bytes/io=0.22K/1.94KparentNaNNaNNaNNaNNaNworker-1-1NaNNaNNaNinfousareur-coi5-nl2020-08-08T08:53:57.734ZNaNNaNcommunicationNaN
3007deaa53585387aa070ec5a2033e4b91596917850132bro-e-communicationU&FOUONaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNbro-e__usareur-coi2-nl__usareur-coi2-nl.2020-08-08-2030.bro-communication.9aa4e.gzNaNNaNNaNNaNNaNNaNNaNNaNNaNparent statistics: pending=0 bytes=14629K/1219913K chunks=170876/1334984 io=78334/1266963 bytes/io=0.19K/0.96K events=67693/60614 operations=0/0parentNaNNaNNaNNaNNaNworker-1-4NaNNaNNaNinfousareur-coi2-nl2020-08-08T20:17:30.132ZNaNNaNcommunicationNaN
4008b98dafd5d34d7a40c76501332fd5f1596928982203bro-e-communicationU&FOUONaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNbro-e__usareur-tlal-nlp__usareur-tlal-nlp.2020-08-08-2330.bro-communication.ac5b0.gzNaNNaNNaNNaNNaNNaNNaNNaNNaNchild statistics: [4] pending=0 bytes=27173966K/3558569K chunks=41080461/31365846 io=20527048/15682924 bytes/io=1.32K/0.23KparentNaNNaNNaNNaNNaNmanagerNaNNaNNaNinfousareur-tlal-nlp2020-08-08T23:23:02.203ZNaNNaNcommunicationNaN
500bf5331ad4f6d6ed85bee75a6c70b691596903834870bro-e-communicationU&FOUONaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNbro-e__usareur-tlal-nlp__usareur-tlal-nlp.2020-08-08-1630.bro-communication.57378.gzNaNNaNNaNNaNNaNNaNNaNNaNNaNparent statistics: pending=0 bytes=3556569K/27114798K chunks=31393574/40852114 io=15654463/25255412 bytes/io=0.23K/1.07K events=15602470/15566275 operations=0/0parentNaNNaNNaNNaNNaNworker-1-6NaNNaNNaNinfousareur-tlal-nlp2020-08-08T16:23:54.87ZNaNNaNcommunicationNaN
600c117654e7053788049b433860be33c1596845288084bro-e-communicationU&FOUONaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNbro-e__usareur-apck-nj__usareur-apck-nj.2020-08-08-0015.bro-communication.787a3.gzNaNNaNNaNNaNNaNNaNNaNNaNNaNchild statistics: [0] pending=0 bytes=0K/0K chunks=10/9 io=3/5 bytes/io=0.08K/0.05KparentNaNNaNNaNNaNNaNworker-1-1NaNNaNNaNinfousareur-apck-nj2020-08-08T00:08:08.084ZNaNNaNcommunicationNaN
700d7db3dd0a84f4f7564289dc03bbe4b1596900201608bro-e-communicationU&FOUONaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNbro-e__usareur-coi3-nl__usareur-coi3-nl.2020-08-08-1530.bro-communication.973a9.gzNaNNaNNaNNaNNaNNaNNaNNaNNaNchild statistics: [0] pending=0 bytes=6886K/364541K chunks=68794/341373 io=33170/170687 bytes/io=0.21K/2.14KparentNaNNaNNaNNaNNaNworker-1-4NaNNaNNaNinfousareur-coi3-nl2020-08-08T15:23:21.608ZNaNNaNcommunicationNaN
800e70f830684623afb22abf7ca76fd8e1596874699705bro-e-communicationU&FOUONaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNbro-e__usareur-coi3-nl__usareur-coi3-nl.2020-08-08-0830.bro-communication.86063.gzNaNNaNNaNNaNNaNNaNNaNNaNNaNchild statistics: [1] pending=0 bytes=0K/0K chunks=9/10 io=3/6 bytes/io=0.08K/0.04KparentNaNNaNNaNNaNNaNproxy-1NaNNaNNaNinfousareur-coi3-nl2020-08-08T08:18:19.705ZNaNNaNcommunicationNaN
900fc05e3e6be90ddde9a61a189243f381596852200088bro-e-communicationU&FOUONaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNbro-e__usareur-coi4-nl__usareur-coi4-nl.2020-08-08-0215.bro-communication.dc0a6.gzNaNNaNNaNNaNNaNNaNNaNNaNNaNparent statistics: pending=0 bytes=1592342K/49451898K chunks=15430440/26093162 io=7410006/18096906 bytes/io=0.21K/2.73K events=7621860/7773834 operations=0/0parentNaNNaNNaNNaNNaNworker-1-4NaNNaNNaNinfousareur-coi4-nl2020-08-08T02:03:20.088ZNaNNaNcommunicationNaN

Last rows

IdTimestampData TypeVisibilityAPPLICATIONPROTOCOLASN_DSTASN_SRCCOCOM_DSTCOCOM_SRCCONNECTIONDETAILCONNECTIONORIGIN_SRCCONNECTIONSTATE_BROCOUNTRY_DSTCOUNTRY_SRCCOUNT_BYTES_INCOUNT_BYTES_IN_ONTHEWIRECOUNT_BYTES_MISSINGCOUNT_BYTES_OUTCOUNT_BYTES_OUT_ONTHEWIRECOUNT_PACKETS_DSTCOUNT_PACKETS_SRCDURATIONDURATION_LOGFILENAME_INGESTIPBRANCHCATEGORY_DSTIPBRANCHCATEGORY_SRCIP_DSTIP_SRCLATITUDE_DSTLATITUDE_SRCLOCAL_TIMESTAMPLONGITUDE_DSTLONGITUDE_SRCMESSAGEMSGSOURCE_BRONUMPACKETS_ACKNUMPACKETS_LOSSNUMPACKETS_LOSS_PERCENTORGANIZATION_OWNER_DSTORGANIZATION_OWNER_SRCPEERNAMEPORT_DSTPORT_SRCRESPONSEORIGIN_DSTSEVERITY_MESSAGESITE_COLLECTIONTIME_RECEIPTTRANSPORTPROTOCOLTUNNELPARENTUUIDSTYPE_BROUUID_BRO
49990C0Xzlx28sycxNjjdze1596899566066bro-e-connU&FOUONaN721.01556.0USNORTHCOMUSEUCOMDdFalseSFUSDE57.085.00.057.085.01.01.00.100119NaNbro-e__usareur-tlas-nj__usareur-tlas-nj.2020-08-08-1515.bro-conn.8abe1.gzNon-ArmyArmy214.71.0.1155.24.116.4337.75100050.0840752020-08-08T17:12:46.066+02:00[Europe/Berlin]-97.822008.25384NaNNaNNaNNaNNaNNaNHQ, 5TH SIGNAL COMMANDNaN53.035432.0FalseNaNusareur-tlas-nj2020-08-08T15:12:46.066ZudpNaNconnC0Xzlx28sycxNjjdze
49991C0XzqC3vHsVM3vl3Q21596898835117bro-e-connU&FOUONaN386.01585.0USNORTHCOMUSEUCOMSrFalseREJUSDE0.040.00.00.048.01.01.00.000772NaNbro-e__usareur-tlaw-nj__usareur-tlaw-nj.2020-08-08-1515.bro-conn.ea828.gzNon-ArmyArmy132.25.191.222155.155.120.637.75100050.0840752020-08-08T17:00:35.117+02:00[Europe/Berlin]-97.822008.25384NaNNaNNaNNaNNaNNaNHQ, 5TH SIGNAL COMMANDNaN8200.060699.0FalseNaNusareur-tlaw-nj2020-08-08T15:00:35.117ZtcpNaNconnC0XzqC3vHsVM3vl3Q2
49992C0XzyHxr4nrkI2Xl1596907562374bro-e-connU&FOUONaN531.0531.0USEUCOMUSEUCOMShADadtFfFalseSFDEDE8485.08977.00.05489.06061.012.014.060.352648NaNbro-e__usareur-tlaw-nj__usareur-tlaw-nj.2020-08-08-1730.bro-conn.e1e18.gzArmyArmy136.215.66.206136.207.68.11450.08407550.0840752020-08-08T19:26:02.374+02:00[Europe/Berlin]8.253848.25384NaNNaNNaNNaNNaNHQ, 5TH SIGNAL COMMANDHQ, 5TH SIGNAL COMMANDNaN80.053237.0FalseNaNusareur-tlaw-nj2020-08-08T17:26:02.374ZtcpNaNconnC0XzyHxr4nrkI2Xl
49993C0XzzjKDc2RQIetKh1596888089238bro-e-connU&FOUONaN30742.01566.0USEUCOMUSEUCOMShADadtFfFalseSFDEDE520471.0537563.00.010970.027342.0427.0409.099.099975NaNbro-e__usareur-tlas-nj__usareur-tlas-nj.2020-08-08-1215.bro-conn.6b54d.gzNon-ArmyArmy185.48.220.16136.221.16.23051.29930050.0840752020-08-08T14:01:29.238+02:00[Europe/Berlin]9.491008.25384NaNNaNNaNNaNNaNNaNHQ, 5TH SIGNAL COMMANDNaN443.061994.0FalseNaNusareur-tlas-nj2020-08-08T12:01:29.238ZtcpNaNconnC0XzzjKDc2RQIetKh
49994C0Y05g2A6r9bL0siB11596867312823bro-e-connU&FOUONaN16509.01566.0USNORTHCOMUSEUCOMShADadFfFalseSFUSDE1553.01925.00.0674.01046.09.09.00.112663NaNbro-e__usareur-tlas-nj__usareur-tlas-nj.2020-08-08-0630.bro-conn.702ce.gzNon-ArmyArmy52.11.247.82136.221.6.20245.84910050.0840752020-08-08T08:15:12.823+02:00[Europe/Berlin]-119.714308.25384NaNNaNNaNNaNNaNNaNHQ, 5TH SIGNAL COMMANDNaN443.064236.0FalseNaNusareur-tlas-nj2020-08-08T06:15:12.823ZtcpNaNconnC0Y05g2A6r9bL0siB1
49995C0Y0G33GMBuXoJqKsl1596880920517bro-e-connU&FOUONaN531.01556.0USEUCOMUSEUCOMDdFalseSFDEDE109.0137.00.056.084.01.01.00.007145NaNbro-e__usareur-tlas-nj__usareur-tlas-nj.2020-08-08-1015.bro-conn.7c1d1.gzArmyArmy139.139.3.147155.24.116.4350.08407550.0840752020-08-08T12:02:00.517+02:00[Europe/Berlin]8.253848.25384NaNNaNNaNNaNNaNHQ, 5TH SIGNAL COMMANDHQ, 5TH SIGNAL COMMANDNaN53.040796.0FalseNaNusareur-tlas-nj2020-08-08T10:02:00.517ZudpNaNconnC0Y0G33GMBuXoJqKsl
49996C0Y0WKc7AwBAOvMUh1596874447321bro-e-connU&FOUONaN1585.01580.0USEUCOMUSEUCOMDdFalseSFDEDE277.0305.00.041.069.01.01.00.001077NaNbro-e__usareur-apck-nj__usareur-apck-nj.2020-08-08-0815.bro-conn.60f1d.gzArmyArmy155.155.72.10147.35.183.4550.08407550.0840752020-08-08T10:14:07.321+02:00[Europe/Berlin]8.253848.25384NaNNaNNaNNaNNaNHQ, 5TH SIGNAL COMMANDHQ, 5TH SIGNAL COMMANDNaN53.059920.0FalseNaNusareur-apck-nj2020-08-08T08:14:07.321ZudpNaNconnC0Y0WKc7AwBAOvMUh
49997C0Y0YO2wQMEeAJKlYb1596898988575bro-e-connU&FOUONaN1585.01580.0USEUCOMUSEUCOMDdFalseSFDEDE64.092.00.048.076.01.01.00.001221NaNbro-e__usareur-apck-nj__usareur-apck-nj.2020-08-08-1515.bro-conn.97cb0.gzArmyArmy155.155.72.10147.35.52.16850.08407550.0840752020-08-08T17:03:08.575+02:00[Europe/Berlin]8.253848.25384NaNNaNNaNNaNNaNHQ, 5TH SIGNAL COMMANDHQ, 5TH SIGNAL COMMANDNaN53.054446.0FalseNaNusareur-apck-nj2020-08-08T15:03:08.575ZudpNaNconnC0Y0YO2wQMEeAJKlYb
49998C0Y0dS1VaHRcOlCMS31596873326762bro-e-connU&FOUONaN531.01580.0USEUCOMUSEUCOMDaFalseOTHDEDE0.040.00.0210.0250.01.01.00.061414NaNbro-e__usareur-apck-nj__usareur-apck-nj.2020-08-08-0815.bro-conn.60f1d.gzArmyArmy136.215.66.201147.35.113.13850.08407550.0840752020-08-08T09:55:26.762+02:00[Europe/Berlin]8.253848.25384NaNNaNNaNNaNNaNHQ, 5TH SIGNAL COMMANDHQ, 5TH SIGNAL COMMANDNaN10123.060116.0FalseNaNusareur-apck-nj2020-08-08T07:55:26.762ZtcpNaNconnC0Y0dS1VaHRcOlCMS3
49999C0Y0hw4QKpYI1ysZa31596863009021bro-e-connU&FOUONaN1559.01585.0USEUCOMUSEUCOMNaNFalseOTHDEDE0.00.00.0112.0168.00.02.03.001537NaNbro-e__usareur-apck-nj__usareur-apck-nj.2020-08-08-0515.bro-conn.68031.gzArmyArmy147.36.249.39155.155.234.2850.08407550.0840752020-08-08T07:03:29.021+02:00[Europe/Berlin]8.253848.25384NaNNaNNaNNaNNaNHQ, 5TH SIGNAL COMMANDHQ, 5TH SIGNAL COMMANDNaN0.08.0FalseNaNusareur-apck-nj2020-08-08T05:03:29.021ZicmpNaNconnC0Y0hw4QKpYI1ysZa3